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2021 IEEE Madras Section International Conference, MASCON 2021

作者： Shettar, Girish B. Devangavi, Anil D. Pareek, Piyush kumar Dept. of Information Science and Engineering Basaveshwar Engineering College Bagalkot India Dept. of Artificial Intelligence & Machine Learning Basaveshwar Engineering College Bagalkot India Dept. of Computer Science and Engineering East West College of Engineering Bangalore India

ISBN: (纸本)9781665404051

The term edge computing is continuously becoming more eminent across the globe as its distributed computing architype which is bringing the computation as well as diverse kind of the data storage nearer to a place wherein this is demanded for enhancing the response times as well as preserve the total available bandwidth. The term MAEC (Multi-Access Edge Computing) providing the cloud computing (CC) competences as well as information technology (IT) services located on edges of diverse networks. The MAEC acts through the effective processing of the diverse kinds of the data sets among the cloud as well as multifarious users worldwide. The multifarious variants, dispersed MAEC, executes over the public 4G as well as 5G networks for linking diverse moving assets as well as things. Although, multifarious investigations are done in prior art for efficient association of the cellular phone subscribers to MAEC in recent decade but existing strategies have numerous limitation namely pragmatic tasks offloading as well as the moving support and many others because of technological alteration. In order to, resolve these existing threats, a novel prototypical architecture is explored in the present research article that more effectively acquire faster response along with uninterrupted service distribution. The outcomes of this proposed prototypical is pragmatic and improved in comparison to the existing approaches explored by multifarious researchers in past decades. Although, considerable research are done in past in this arena for resolving the encountered threats but still there is mammoth scope to deep dive in this domain for pragmatic outcomes. © 2021 IEEE.

关键词： Network architecture

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Diverse and Relevant Visual Storytelling with Scene Graph Embeddings 24

Diverse and Relevant Visual Storytelling with Scene Graph Em...

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24th Conference on Computational Natural Language learning, CoNLL 2020

作者： Hong, Xudong Shetty, Rakshith Sayeed, Asad Mehra, Khushboo Demberg, Vera Schiele, Bernt Dept. of Computer Vision and Machine Learning MPI Informatics Germany Dept. of Language Science and Technology Saarland University Germany Dept. of Philosophy Linguistics and Theory of Science University of Gothenburg Sweden

ISBN: (纸本)9781952148637

A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text. We address this problem by introducing explicit representations for objects and their relations by extracting scene graphs from the images. Utilizing an embedding of this scene graph enables our model to more explicitly reason over objects and their relations during story generation, compared to the global features from an object classifier used in previous work. We apply metrics that account for the diversity of words and phrases of generated stories as well as for reference to narratively-salient image features and show that our approach outperforms previous systems. Our experiments also indicate that our models obtain competitive results on reference-based metrics. © 2020 Association for Computational Linguistics.

关键词： Graph embeddings

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Exploring Neutrosophic Numeral System Algorithms for Handling Uncertainty and Ambiguity in Numerical Data: An Overview and Future Directions

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Neutrosophic Sets and Systems 2024年 65卷 253-295页

作者： Salama, A.A. Shams, Mahmoud Y. Elseuofi, Sherif Khalid, Huda E. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Information System Department Higher Institute for Computers & Specific Studies Ras-Elbar Damietta Egypt Telafer University The Administration Assistant for the President of the Telafer University Telafer Iraq

The Neutrosophic Numeral System Algorithms are a set of techniques designed to handle uncertainty and ambiguity in numerical data. These algorithms use Neutrosophic Set Theory, a mathematical framework that deals with incomplete, indeterminate, and inconsistent information. In this paper, we provide an overview of different approaches used in Neutrosophic Numeral System Algorithms, including Neutrosophic Binary System, Neutrosophic Decimal System, and Neutrosophic Octal System. These systems use different bases and representations to account for degrees of truth, indeterminacy, and falsity in numerical data. We also explore the relationship between Neutrosophic Numeral System Algorithms and Number Neutrosophic Systems, which are another type of Neutrosophic System used for representing numerical data. Number Neutrosophic Systems use Neutrosophic Numbers to represent degrees of truth, indeterminacy, and falsity in numerical data, and they can be used in conjunction with Neutrosophic Numeral System Algorithms to handle uncertainty and ambiguity in decision-making and artificial intelligence applications. Moreover. We discuss the advantages and disadvantages of each algorithm and their potential applications in various fields. Finally, we highlight the importance of Neutrosophic cryptography in addressing uncertainty and ambiguity in decision making and artificial intelligence and discuss future research directions. Understanding Neutrosophic Numeral System Algorithms and their relationship with Number Neutrosophic Systems is crucial for developing effective techniques for handling uncertainty and ambiguity in numerical data in decision-making, pattern recognition, and artificial intelligence applications. © (2024) All Rights Reserved.

关键词： Decision making

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Human Activity Classification using G-XGB

Human Activity Classification using G-XGB

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Kataria, Aman Puri, Vikram Pareek, Piyush Kumar Rani, Sita Amity Institute of Defence Technology Amity University Noida Campus Sector-125 Noida India Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam Cell Nitte Meenakshi Institute of Technology Dept. of Artificial Intelligence and Machine Learning and Ipr Bengaluru India Guru Nanak Dev Engineering College Department of Computer Science and Engineering Ludhiana India

ISBN: (纸本)9798350301595

Wearable body sensors have recently gained popularity because they monitor user behaviors and vital indicators. These sensors are designed to monitor various physiological factors, such as motion, temperature, and heart rate, among others. Making sense of the data gathered by these sensors requires separating the dependent and independent features. The dependent parameters are those that body-worn sensors can read in this scenario. Examples of such elements include blood oxygen levels, heart rate, temperature, mobility, and airflow. These sensors capture data about a person's activity and health. The independent parameters in wearable body sensors are related to the dependent parameters, whereas the dependent parameters are those that the sensors can measure directly. Such characteristics include age, sex, weight, and environmental influences. For example, humidity and airflow might impair a temperature sensor's accuracy. The user's age or level of physical fitness may also influence the accuracy of a heart rate monitor. Activity monitors are used in this study to capture the actions of various people. Static and dynamic properties are derived from the activity monitor recording. The performance evaluations of the eight distinct machine-learning models that were created using the retrieved features are discussed in this paper. The Grid Search method is used to enhance the performance of the XGB model. © 2023 IEEE.

关键词： Heart

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Smart Locker 2.0: Leveraging IoT and machine learning for Secure, User-Friendly Public Storage

Smart Locker 2.0: Leveraging IoT and Machine Learning for Se...

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Artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： Chandrappa S Prithviraj Aditya Pai H Rajesh I S Manjunath Sargur Krishnamurthy R Sneha Shetty R Dept. of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India Dept. of Computer Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte India Department of CSE MIT Art Design and Technology University Pune India Dept. of AI & ML BMS Institute of Technology and Management Bengaluru India JP Morgan & Chase Co Houston USA Dept. of Artificial Intelligence and Machine Learning Nitte (Deemed to be University) NMAM Institute of Technology Nitte India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Smart locker systems are a revolutionary technology, combining advanced automation and connectivity to address increasingly demanding requirements for secure, efficient, and convenient storage solutions in numerous scenarios. These intelligent lockers use the latest technologies, such as the Internet of Things (IoT) and mobile applications, to revolutionize the way people store their belongings. Existing Smart Locker System lacks measurable results. To sustain its claims, we include key findings. For security, the system utilizes RFID authentication and biometric security, which reduces unauthorized access by 98% and blocks spoofing attacks. For convenience, a study with 50 users found 92% satisfaction, with 87% preferring it over traditional lockers due to faster access, averaging 3 seconds. For environmental benefits, the system cut power use by using sleep mechanism and saved paper usage by switching to cloud logs. Real-World tests achieved 98% user satisfaction with the university system proving its efficiency, security, and sustainability. This system expounds on multifaceted benefits and functionalities in smart lockers. First, it covers how these lockers provide better security compared to old lock-and-key systems. Using access controls reduces the risk of theft and illegal access to one's valuables and sensitive belongings by a great degree. It has an easy reservation, access, and management of personal lockers via a remote means; this proves to be seamless without needing any physical keys and contactless interactions, ideal for modern spaces considering hygiene and efficiency is top priority. Moreover, it goes on to analyze the flexibility of smart locker systems. From corporate offices to co-working spaces, universities, shopping malls, and public transportation hubs, these lockers cater to diverse user needs, creating flexible and scalable solutions adaptable to a variety of environments.

关键词： Biometrics Cloud computing Switches machine learning Mobile applications Internet of Things Sustainable development Secure storage Public transportation Radiofrequency identification

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Integrated Sensing and Communications for Unsourced Random Access: Fundamental Limits

Integrated Sensing and Communications for Unsourced Random A...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mohammad Javad Ahmadi Rafael F. Schaefer H. Vincent Poor Chair of Information Theory and Machine Learning Technische Universität Dresden Cluster of Excellence CeTI BMBF Research Hub 6G-Life Barkhausen Institut Dresden Germany Dept. of Electrical and Computer Engineering Princeton University

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This work considers the problem of integrated sensing and communications (ISAC) with a massive number of unsourced and uncoordinated users. In the proposed model, known as the unsourced ISAC system (UNISAC), all active communication and sensing users simultaneously share a short frame to transmit their signals, without requiring scheduling with the base station (BS). Hence, the signal received from each user is affected by significant interference from numerous interfering users, making it challenging to extract the transmitted signals. UNISAC aims to decode the transmitted message sequences from communication users while simultaneously detecting active sensing users and estimating their angles of arrival, regardless of the identity of the senders. In this paper, we derive an approximate achievable result for UNISAC and demonstrate its superiority over conventional approaches such as ALOHA, time-division multiple access, treating interference as noise, and multiple signal classification. Through numerical simulations, we validate the effectiveness of UNISAC’s sensing and communication capabilities for a large number of users.

关键词： Base stations Time division multiple access Computational modeling Noise Pattern classification Interference Tin Integrated sensing and communication Numerical simulation Multiple signal classification

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End to End Model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks

End to End Model to Reduce the Inference, Jamming, and to In...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shashank Dhananjaya Narender M Sunitha R Department of Information Science and Engineering The National Institute of Engineering Mysore Department of Computer Science and Engineering The National Institute of Engineering Mysore Dept. of Artificial Intelligence and Machine Learning B. N. M. Institute of Technology

Wireless technology has advanced dramatically in recent years. Multimedia applications have exploded in popularity over wireless networks. The demand for radio spectrum has expanded as the demand for large capacity for multimedia applications has grown. The traditional fixed spectrum assignment approach has been challenged by the shortage of radio spectrum. As a result, Cognitive Radio (CR) has arisen as a new unique option for addressing spectrum underutilization by allowing users to access unused spectrum bands on the fly. In cognitive radio networks, radio spectrum scarcity, spectrum access, signal interference, security at various layers, trust, jamming, and spectrum management have all become key issues. Proposing a unique approach to address the interference problem produced by secondary nodes that have been hacked. To fulfil the need, we develop a mathematical modelling of the system that incorporates intelligence in the SU to cope with intelligent jammer. Intelligent jammer is cognitive in nature, having the ability to learn, to reason and to adjust its strategies against SU for maximum damage to the CRN. Many researchers have been working on reducing inference and the effect of jamming in the network. But still it is not resulted with the proper solution for the jamming problem in cognitive radio wireless sensor network. Hence proposing the methodology aiming to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks. At the end, the proposed research is aiming to solve the security problem to increase the trust in the cognitive radio network. The secondary users must be able to detect and forecast spectrum holes in the CR environment. The overall result shows the improved network performance by reducing the inference, jamming, spectrum sensing and trust issues in cognitive radio network.

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Indian Food Segmentation and Calorie Estimation Using CatBoost and Masked Convolutional Neural Networks

Indian Food Segmentation and Calorie Estimation Using CatBoo...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Nabin Acharya Ayush Kumar Singh Anshuman Kumar Dwivedi Jayanthi M. G Prashant Kannadaguli Dept. of Information Science and Engineering Cambridge Institute of Technology Bengaluru India Dept. of Artificial Intelligence and Machine Learning Cambridge Institute of Technology Bengaluru India Dept. of Computer Science and Engineering Cambridge Institute of Technology Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

Accurate food segmentation and calorie estimation play a pivotal role in effective dietary monitoring and health management. In this paper, after extensive experimentation on a diverse Indian food dataset of 20 classes collected by us, we present two separate innovative models designed to address these tasks with exceptional precision. The first model employs a machine learning approach by incorporating the CatBoost algorithm, while the second model leverages a deep learning technique utilizing the U-Net architecture for high-quality image segmentation. Additionally, we merge the U-Net outcomes with a convolutional neural network (CNN) to enhance the deep learning-based classification. Our models effectively handle categorical features and address imbalanced data, resulting in significantly improved accuracy in food item delineation within images. This advancement enables a more reliable and comprehensive analysis of dietary patterns. We demonstrate the effectiveness and robustness of our proposed models. Comparative evaluations against state-of-the-art methods verify exceptional performance in terms of segmentation accuracy, precision in food classification, and estimation of calorie intake. Notably, our models are specifically tailored for food images and do not take ingredients or any other information into consideration. In comparison CNN model is performs better than CatBoost, achieving an accuracy of 78%.

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Lung Nodule Segmentation and Classification Using Conv-Unet Based Deep learning

Lung Nodule Segmentation and Classification Using Conv-Unet ...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Akash V Monish Sai. R Amith K B Jayanthi M.G. Prashanth Kannadaguli Dept. of Artificial Intelligence and Machine Learning Cambridge Institute of Technology Bangalore India Dept. of Information Science and Engineering Cambridge Institute of Technology Bangalore India Dept. of Computer Science and Engineering Cambridge Institute of Technology Bangalore India Dhaarini Academy of Technical Education Bangalore India

This paper proposes a Convolutional U-Net architecture, a variation of the standard U-Net architecture for the segmentation of lung nodules and classification using Deep learning on computerized Tomography (CT) scans. The Primary steps employed are Preprocessing, Segmentation and Classification of nodules. In the preprocessing step, the lung region is segmented using techniques such as normalization, median filtering, Kmeans clustering, morphological and thresholding operations to extract lung Region of Interest (ROI) and nodule masks. The Conv-Unet design adds more convolutional layers to the standard U -Net architecture to help capture complicated patterns and boundaries of lung nodules for more accurate segmentation. Categorization of the segmented lung nodules is done using a CNN network on the LIDC-IDRI, and LUNA16 dataset. Overall, this model achieves a dice score of 62% and classification accuracy of 82% displaying appropriate performance in comparison with other variations of the U-Net architecture.

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